Clients ask us this constantly: should we fine-tune or use RAG? The honest answer is that it depends on your data freshness requirements, budget, and how specialized your domain is.
When RAG Wins
Use retrieval-augmented generation when your knowledge base changes frequently — policy docs, product catalogs, support articles. RAG keeps answers current without retraining.
When Fine-Tuning Wins
Fine-tuning makes sense when you need consistent tone, format, or domain-specific reasoning that prompting alone cannot achieve. Think legal summaries, medical triage, or branded customer support.
The Hybrid Path
Most enterprise deployments we build combine both: a fine-tuned model for voice and structure, RAG for factual grounding. Start simple, add complexity only when metrics justify it.